Article
Management
Rafael A. Melo, Michell F. Queiroz, Marcio C. Santos
Summary: The study introduces a new approach to solve the b-coloring problem, demonstrating the effectiveness of the multi-start metaheuristic algorithm and the improvement achieved by the matheuristic approach. Additionally, a benchmark instance set is proposed for standardized computational comparisons in future works.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Luana Souza Almeida, Floris Goerlandt, Ronald Pelot, Kenneth Sorensen
Summary: This article investigates the severe impact of natural disasters, such as earthquakes, on road networks. It proposes a metaheuristic algorithm for solving the problem of road connectivity. Through testing on randomly generated instances of increasing size, the results indicate that the proposed algorithm outperforms the previous mathematical heuristic in terms of objective function values and execution time.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Miguel Angel Rodriguez-Garcia, Jesus Sanchez-Oro, Eduardo Rodriguez-Tello, Eric Monfroy, Abraham Duarte
Summary: The research focuses on the bandwidth optimization problem of embedding a graph in a two-dimensional grid, with CSP models showing remarkable performance in small to medium instances, while BVNS is capable of achieving equivalent or similar results in short run-time for small instances.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Mathematics
Juan F. Gomez, Javier Panadero, Rafael D. Tordecilla, Juliana Castaneda, Angel A. Juan
Summary: The capacitated dispersion problem involves selecting a subset of elements in a network to maximize the minimum distance between any pair of elements while reaching an aggregated servicing capacity. It is a more realistic approach than the traditional maximum diversity problem and requires the use of heuristic algorithms for large-sized instances.
Article
Mathematics
Diego Noceda-Davila, Silvia Lorenzo-Freire, Luisa Carpente
Summary: This paper presents new methods for solving the optimization problem of DNA sample allocation in plates for Sanger DNA sequencing. These methods work with independent subproblems of lower complexity, resulting in high-quality solutions within a competitive time frame. Comparative analyses with existing literature methods using real data demonstrate interesting results.
Article
Computer Science, Artificial Intelligence
Masoud Shahmanzari, Deniz Aksen
Summary: This paper introduces a novel algorithm MS-GSVNTS for solving the Roaming Salesman Problem (RSP). Computational results show that MS-GSVNTS, tested on actual travel distances and times, outperforms existing solution methods for RSP.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Nicolas R. Uribe, Alberto Herran, J. Manuel Colmenar, Abraham Duarte
Summary: In this paper, the focus is on the Multiple Row Equal Facility Layout Problem (MREFLP) and a Greedy Randomized Adaptive Search Procedure (GRASP) is proposed. Through preliminary experimentation, it was shown that GRASP finds better results spending much less execution time compared to current state-of-the-art algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Ece Yagmur, Saadettin Erhan Kesen
Summary: The study investigates a joint production scheduling and outbound distribution planning problem, using a mixed integer programming formulation and genetic algorithm to reduce delivery delays and vehicle travel time, proposing a new splitting procedure. Experimental results indicate that genetic algorithm outperforms simulated annealing in terms of solution quality for medium and large instances.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Bruno Jose da Silva Barros, Rian Gabriel S. Pinheiro, Ueverton S. Souza, Luiz Satoru Ochi
Summary: This paper presents a method for solving the Minimum Conflict-Free Spanning Tree problem using the GRASP-AM algorithm, and experimental results demonstrate its superiority over other heuristic methods.
Article
Computer Science, Artificial Intelligence
Marko Djukanovic, Aleksandar Kartelj, Christian Blum
Summary: This paper presents an algorithm for the multidimensional multi-way number partitioning problem, which aims to divide a set of vectors into non-empty subsets with similar coordinate sums. The algorithm outperforms four competing algorithms from the literature, especially for instances with higher k-values. Experimental evaluation shows that the proposed algorithm achieves average relative differences larger than 25% compared to the second-best approach, and significantly outperforms other approaches for all instances with k & GE; 3.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Metallurgy & Metallurgical Engineering
Akira Kumano, Yusuke Yoshinari, Osamu Yamaguchi, Toru Miyazawa
Summary: This article introduces an optimal scheduling method for multiple stockpiles in a stockyard, aiming to improve logistics efficiency and operational stability for steelworks. The developed Stockpile Layout Planner utilizes a multi-start Greedy algorithm to optimize yard operations and ensure efficient ore management.
TETSU TO HAGANE-JOURNAL OF THE IRON AND STEEL INSTITUTE OF JAPAN
(2023)
Article
Engineering, Industrial
Xing Wan, Xingquan Zuo, Xiaodong Li, Xinchao Zhao
Summary: The multi-row facility layout problem (MRLP) is an important design problem in real life. Existing studies often overlook clearances between machines or only consider minimum clearances. This paper proposes using larger clearances between adjacent machines to achieve lower material flow cost, while optimizing layout area. By combining mixed integer programming and multi-objective greedy randomised adaptive search procedure, an effective solution is provided.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Management
J. Sanchez-Oro, A. D. Lopez-Sanchez, A. G. Hernandez-Diaz, A. Duarte
Summary: This paper presents a competitive algorithm that combines the Greedy Randomized Adaptive Search Procedure and Tabu Search, along with Strategic Oscillation post-processing, to provide high-quality solutions for the alpha-neighbor p-center problem. Extensive comparison shows the relevance of the proposed algorithm in achieving competitive results.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Alejandra Casado, Sergio Perez-Pelo, Jesus Sanchez-Oro, Abraham Duarte
Summary: This research focuses on the maximum intersection of the k-subsets problem (kMIS) and proposes an improved search algorithm with a novel representation method for solutions. Experimental results confirm the superiority of the proposed method.
JOURNAL OF HEURISTICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Meryem Bamoumen, Selwa Elfirdoussi, Libo Ren, Nikolay Tchernev
Summary: This work focuses on the scheduling problem of a multi-product straight pipeline system. The goal is to find a batch sequence that maximizes the total volume transported through the pipeline while meeting daily customer demands. Constraints related to inventory levels, batch settling periods, product sequences, and pipeline stoppage periods are considered. A MILP model and a GRASP-like algorithm are proposed, and numerical experiments demonstrate their competitiveness in terms of solution quality and computational time.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Management
Juanjo Peiro, Iris Jimenez, Jose Laguardia, Rafael Marti
Summary: This paper investigates the adaptation of GRASP and VND methodologies to the capacitated dispersion problem, proposing a hybrid algorithm within the strategic oscillation framework. Extensive experimentation and mathematical modeling are used to evaluate the algorithm's performance. Comparisons with existing software solutions are also made.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2021)
Article
Management
Rafael B. Velasco, Igor Carpanese, Ruben Interian, Octavio C. G. Paulo Neto, Celso C. Ribeiro
Summary: Investigators in Brazil have uncovered widespread corruption and money laundering schemes in government and corporations, resulting in significant annual global GDP losses. Most law enforcement agencies lack the capability to assess corruption risks systematically. The decision support system described in this work addresses these limitations by providing a tool for systematic analysis of public procurement, leading to improved quality of public spending and identification of more fraud cases.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2021)
Article
Management
Rafael A. Melo, Michell F. Queiroz, Celso C. Ribeiro
Summary: This paper introduces a new method to solve the minimum weighted feedback vertex set problem by tackling it through the maximum weighted induced forest problem. Through a matheuristic approach, the method is able to efficiently solve a large number of test instances in a short amount of time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Review
Management
Francisco Parreno, Ramon Alvarez-Valdes, Rafael Marti
Summary: This paper examines four mathematical models for achieving diversity, comparing their solutions over the MDPLIB library. By adding new Euclidean instances, the study analyzes the geometric distribution of solutions. The research identifies which models are better suited for dispersion or representativeness, as well as challenging instances and a model that is not recommended in any setting.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Rafael Marti, Anna Martinez-Gavara, Jesus Sanchez-Oro
Summary: This study focuses on a variant recently introduced that includes capacity values, proposing a mathematical model and a heuristic based on Scatter Search methodology to maximize diversity while satisfying capacity constraints.Scatter search is a memetic algorithm hybridizing evolutionary global search with a problem-specific local search.
Article
Operations Research & Management Science
Angel A. Juan, Peter Keenan, Rafael Marti, Sean McGarraghy, Javier Panadero, Paula Carroll, Diego Oliva
Summary: In the context of simulation-based optimization, this paper reviews recent work related to metaheuristics, matheuristics, simheuristics, biased-randomised heuristics, and learnheuristics for solving complex and large-scale optimization problems in various domains. The paper provides an overview of the main concepts and updated references, and highlights the applications of these hybrid optimization-simulation-learning approaches in solving real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits across different application fields. The paper concludes by highlighting open research lines on extending the concept of simulation-based optimization.
ANNALS OF OPERATIONS RESEARCH
(2023)
Review
Management
Rafael Marti, Anna Martinez-Gavara, Sergio Perez-Pelo, Jesus Sanchez-Oro
Summary: This paper focuses on the problem of selecting a subset of elements from a given set in order to maximize the distance among the selected elements. The milestones in the development of this area are reviewed, and the connection and challenges among different models are analyzed. The benchmark instances are also revised and extended, and the best and more recently proposed procedures are empirically reviewed and compared to identify the state-of-the-art methods for the main diversity models.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Review
Management
Rafael A. Melo, Celso C. Ribeiro
Summary: This paper introduces the maximum weighted induced forest and tree problems, proposes two new integer programming formulations, and compares them with various existing methods. Experimental results show that the new formulations offer stronger linear relaxation bounds and better performance in terms of proving optimality time.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2022)
Review
Management
Carlos R. H. Marquez, Celso C. Ribeiro
Summary: This article reviews the literature on shop scheduling problems in manufacturing systems, highlighting the concepts and methodologies that have the greatest impact on the application of scheduling theory in manufacturing environments. The focus is on job shop and flow shop problems and their variants, as well as the interactions with manufacturing paradigms such as Industry 4.0. The main components and characteristics of the scheduling ecosystem are described, along with their interactions and influences.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Ruslan G. Marzo, Rafael A. Melo, Celso C. Ribeiro, Marcio C. Santos
Summary: This paper introduces two new formulations, cec and cut, for solving the longest induced path problem. Experimental results show that, despite being less strong theoretically, cec performs the best in practical applications.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Isaac Lozano-Osorio, Anna Martinez-Gavara, Rafael Marti, Abraham Duarte
Summary: This study focuses on diversity and dispersion problems and proposes linear formulations and a hybrid metaheuristic algorithm to solve the generalized dispersion problem. Computational experiments show the superiority of the proposed algorithm in real instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Yogita Singh Kardam, Kamal Srivastava, Pallavi Jain, Rafael Marti
Summary: This paper proposes a heuristic algorithm based on scatter search methodology for the Minimum Leaf Spanning Tree Problem (MLSTP), which is capable of generating spanning trees with a lower number of leaves compared to previous methods.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Economics
Zhi-Long Dong, Celso C. Ribeiro, Fengmin Xu, Ailec Zamora, Yujie Ma, Kui Jing
Summary: Electronic sports tournaments are effectively scheduled using a dynamic approach based on a modified Swiss system design. Colley's method is used to update ratings for all competitors, ensuring game fairness and viewers' satisfaction in each round. The approach's applicability is validated using real-life data from the 2020 Honor of Kings World Champion Cup group stage and further evaluated with randomly generated test problems involving up to 80 competitors.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Operations Research & Management Science
Rafael A. Melo, Celso C. Ribeiro, Jose A. Riveaux
Summary: This paper tackles the minimum quasi-clique partitioning problem and proposes a biased random-key genetic algorithm (BRKGA) that can obtain high-quality solutions in low computational times. Furthermore, it is shown that MQCPP and the problem of covering the graph with a minimum number of quasi-cliques are not equivalent.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Miguel Reula, Rafael Marti
Summary: In this paper, we propose a heuristic approach based on variable neighborhood search methodology to solve the profitable close-enough arc routing problem. Our method aims to maximize the sum of profits of the clients served while considering the distance traveled. We conducted extensive experimentation to compare our approach with state-of-the-art heuristics, and the results confirm that our algorithm outperforms previous algorithms for this problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)